/**
 * @file SelfImprovementMetrics - Comprehensive metrics and feedback loop system
 * 
 * This system implements comprehensive metrics collection for self-improvement tracking,
 * A/B testing framework for prompt optimization, and automated feedback loops that
 * continuously enhance the Universal AI Brain's performance using MongoDB analytics.
 * 
 * Features:
 * - Comprehensive performance metrics tracking
 * - A/B testing framework for prompt optimization
 * - Automated feedback loops and improvement cycles
 * - Real-time improvement analytics with MongoDB
 * - Cross-framework performance comparison
 * - Predictive improvement modeling
 * - Automated optimization triggers
 */

import { TracingCollection, AgentTrace } from '../collections/TracingCollection';
import { MemoryCollection } from '../collections/MemoryCollection';
import { FailureAnalysisEngine } from './FailureAnalysisEngine';
import { ContextLearningEngine } from './ContextLearningEngine';
import { FrameworkOptimizationEngine } from './FrameworkOptimizationEngine';

export interface ImprovementMetrics {
  metricId: string;
  timestamp: Date;
  timeRange: {
    start: Date;
    end: Date;
  };
  overallPerformance: {
    responseTime: {
      current: number;
      baseline: number;
      improvement: number;
      trend: 'improving' | 'stable' | 'declining';
    };
    accuracy: {
      current: number;
      baseline: number;
      improvement: number;
      trend: 'improving' | 'stable' | 'declining';
    };
    userSatisfaction: {
      current: number;
      baseline: number;
      improvement: number;
      trend: 'improving' | 'stable' | 'declining';
    };
    costEfficiency: {
      current: number;
      baseline: number;
      improvement: number;
      trend: 'improving' | 'stable' | 'declining';
    };
  };
  frameworkMetrics: {
    framework: string;
    performanceScore: number;
    improvementRate: number;
    optimizationCount: number;
    lastOptimized: Date;
  }[];
  improvementAreas: {
    area: 'context_relevance' | 'prompt_optimization' | 'parameter_tuning' | 'error_reduction' | 'cost_optimization';
    currentScore: number;
    targetScore: number;
    progress: number;
    priority: 'high' | 'medium' | 'low';
    estimatedCompletion: Date;
  }[];
  feedbackLoops: {
    loopId: string;
    type: 'automated' | 'user_feedback' | 'performance_based' | 'error_triggered';
    status: 'active' | 'paused' | 'completed';
    triggerCondition: string;
    lastTriggered: Date;
    improvementGenerated: number;
  }[];
}

export interface ABTestResult {
  testId: string;
  testName: string;
  startDate: Date;
  endDate: Date;
  variants: {
    variantId: string;
    name: string;
    configuration: any;
    sampleSize: number;
    metrics: {
      responseTime: number;
      accuracy: number;
      userSatisfaction: number;
      costPerOperation: number;
      errorRate: number;
    };
    statisticalSignificance: number;
  }[];
  winner: {
    variantId: string;
    confidenceLevel: number;
    improvementPercentage: number;
  };
  status: 'running' | 'completed' | 'paused' | 'cancelled';
}

export interface FeedbackLoop {
  loopId: string;
  name: string;
  type: 'automated' | 'user_feedback' | 'performance_based' | 'error_triggered';
  triggerConditions: {
    metric: string;
    threshold: number;
    operator: 'gt' | 'lt' | 'eq' | 'gte' | 'lte';
    timeWindow: number; // minutes
  }[];
  actions: {
    actionType: 'optimize_parameters' | 'retrain_model' | 'update_prompts' | 'adjust_context' | 'alert_human';
    parameters: Record<string, any>;
    priority: number;
  }[];
  isActive: boolean;
  lastTriggered?: Date;
  triggerCount: number;
  successRate: number;
}

export interface ImprovementPrediction {
  predictionId: string;
  timestamp: Date;
  timeHorizon: number; // days
  predictedImprovements: {
    metric: string;
    currentValue: number;
    predictedValue: number;
    confidence: number;
    factors: string[];
  }[];
  recommendedActions: {
    action: string;
    priority: 'immediate' | 'high' | 'medium' | 'low';
    expectedImpact: number;
    estimatedEffort: 'low' | 'medium' | 'high';
    timeline: string;
  }[];
}

/**
 * SelfImprovementMetrics - Comprehensive metrics and feedback loop system
 * 
 * Tracks improvement progress, runs A/B tests, and creates automated feedback
 * loops for continuous enhancement of the Universal AI Brain.
 */
export class SelfImprovementMetrics {
  private tracingCollection: TracingCollection;
  private memoryCollection: MemoryCollection;
  private failureAnalysisEngine: FailureAnalysisEngine;
  private contextLearningEngine: ContextLearningEngine;
  private frameworkOptimizationEngine: FrameworkOptimizationEngine;
  private activeFeedbackLoops: Map<string, FeedbackLoop> = new Map();
  private activeABTests: Map<string, ABTestResult> = new Map();

  constructor(
    tracingCollection: TracingCollection,
    memoryCollection: MemoryCollection,
    failureAnalysisEngine: FailureAnalysisEngine,
    contextLearningEngine: ContextLearningEngine,
    frameworkOptimizationEngine: FrameworkOptimizationEngine
  ) {
    this.tracingCollection = tracingCollection;
    this.memoryCollection = memoryCollection;
    this.failureAnalysisEngine = failureAnalysisEngine;
    this.contextLearningEngine = contextLearningEngine;
    this.frameworkOptimizationEngine = frameworkOptimizationEngine;
    this.initializeFeedbackLoops();
  }

  /**
   * Generate comprehensive improvement metrics using MongoDB aggregation
   */
  async generateImprovementMetrics(timeRange: { start: Date; end: Date }): Promise<ImprovementMetrics> {
    // Calculate baseline metrics from earlier period
    const baselineRange = {
      start: new Date(timeRange.start.getTime() - (timeRange.end.getTime() - timeRange.start.getTime())),
      end: timeRange.start
    };

    // Use MongoDB $facet aggregation for comprehensive metrics analysis
    const metricsPipeline = [
      {
        $match: {
          startTime: { $gte: timeRange.start, $lte: timeRange.end }
        }
      },
      {
        $facet: {
          // Overall performance metrics
          performanceMetrics: [
            {
              $group: {
                _id: null,
                avgResponseTime: { $avg: '$performance.totalDuration' },
                avgAccuracy: { $avg: '$feedback.accuracy' },
                avgSatisfaction: { $avg: '$feedback.rating' },
                avgCost: { $avg: '$cost.total' },
                totalOperations: { $sum: 1 },
                successfulOperations: {
                  $sum: { $cond: [{ $eq: ['$status', 'completed'] }, 1, 0] }
                }
              }
            }
          ],

          // Framework-specific performance
          frameworkPerformance: [
            {
              $group: {
                _id: '$framework.frameworkName',
                avgResponseTime: { $avg: '$performance.totalDuration' },
                avgAccuracy: { $avg: '$feedback.accuracy' },
                avgSatisfaction: { $avg: '$feedback.rating' },
                operationCount: { $sum: 1 },
                errorCount: {
                  $sum: { $cond: [{ $gt: [{ $size: { $ifNull: ['$errors', []] } }, 0] }, 1, 0] }
                }
              }
            },
            {
              $addFields: {
                performanceScore: {
                  $multiply: [
                    { $divide: ['$avgAccuracy', 100] },
                    { $divide: [2000, { $add: ['$avgResponseTime', 1] }] },
                    { $divide: ['$avgSatisfaction', 5] }
                  ]
                },
                errorRate: { $divide: ['$errorCount', '$operationCount'] }
              }
            },
            { $sort: { performanceScore: -1 } }
          ],

          // Improvement trends over time
          improvementTrends: [
            {
              $group: {
                _id: {
                  $dateToString: {
                    format: '%Y-%m-%d',
                    date: '$startTime'
                  }
                },
                avgResponseTime: { $avg: '$performance.totalDuration' },
                avgAccuracy: { $avg: '$feedback.accuracy' },
                avgSatisfaction: { $avg: '$feedback.rating' },
                operationCount: { $sum: 1 }
              }
            },
            { $sort: { '_id': 1 } }
          ],

          // Context relevance improvements
          contextMetrics: [
            {
              $match: {
                contextUsed: { $exists: true, $ne: [] }
              }
            },
            {
              $group: {
                _id: null,
                avgRelevanceScore: { $avg: '$contextUsed.relevanceScore' },
                avgContextCount: { $avg: { $size: '$contextUsed' } },
                contextSuccessRate: {
                  $avg: {
                    $cond: [{ $gte: ['$contextUsed.relevanceScore', 0.7] }, 1, 0]
                  }
                }
              }
            }
          ]
        }
      }
    ];

    const currentMetrics = await this.tracingCollection.aggregate(metricsPipeline);
    const baselineMetrics = await this.getBaselineMetrics(baselineRange);

    // Calculate improvements and trends
    const overallPerformance = this.calculatePerformanceImprovements(
      currentMetrics[0].performanceMetrics[0],
      baselineMetrics.performanceMetrics
    );

    // Get framework metrics
    const frameworkMetrics = currentMetrics[0].frameworkPerformance.map((framework: any) => ({
      framework: framework._id,
      performanceScore: Math.round(framework.performanceScore * 100) / 100,
      improvementRate: this.calculateImprovementRate(framework._id, timeRange),
      optimizationCount: this.getOptimizationCount(framework._id, timeRange),
      lastOptimized: new Date() // Would get actual last optimization date
    }));

    // Analyze improvement areas
    const improvementAreas = await this.analyzeImprovementAreas(currentMetrics[0]);

    // Get active feedback loops status
    const feedbackLoops = Array.from(this.activeFeedbackLoops.values()).map(loop => ({
      loopId: loop.loopId,
      type: loop.type,
      status: (loop.isActive ? 'active' : 'paused') as 'active' | 'paused' | 'completed',
      triggerCondition: loop.triggerConditions.map(c => `${c.metric} ${c.operator} ${c.threshold}`).join(' AND '),
      lastTriggered: loop.lastTriggered || new Date(),
      improvementGenerated: loop.successRate * 10 // Simplified calculation
    }));

    return {
      metricId: `metrics_${Date.now()}`,
      timestamp: new Date(),
      timeRange,
      overallPerformance,
      frameworkMetrics,
      improvementAreas,
      feedbackLoops
    };
  }

  /**
   * Start A/B test for prompt optimization
   */
  async startABTest(
    testName: string,
    variants: {
      name: string;
      configuration: any;
    }[],
    duration: number = 7 // days
  ): Promise<string> {
    const testId = `ab_test_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
    const startDate = new Date();
    const endDate = new Date(startDate.getTime() + duration * 24 * 60 * 60 * 1000);

    const abTest: ABTestResult = {
      testId,
      testName,
      startDate,
      endDate,
      variants: variants.map((variant, index) => ({
        variantId: `variant_${index}`,
        name: variant.name,
        configuration: variant.configuration,
        sampleSize: 0,
        metrics: {
          responseTime: 0,
          accuracy: 0,
          userSatisfaction: 0,
          costPerOperation: 0,
          errorRate: 0
        },
        statisticalSignificance: 0
      })),
      winner: {
        variantId: '',
        confidenceLevel: 0,
        improvementPercentage: 0
      },
      status: 'running'
    };

    this.activeABTests.set(testId, abTest);

    // Store A/B test in MongoDB
    await this.memoryCollection.storeDocument(
      JSON.stringify(abTest),
      {
        type: 'ab_test',
        testId,
        testName,
        status: 'running',
        startDate,
        endDate
      }
    );

    return testId;
  }

  /**
   * Create automated feedback loop
   */
  async createFeedbackLoop(
    name: string,
    type: FeedbackLoop['type'],
    triggerConditions: FeedbackLoop['triggerConditions'],
    actions: FeedbackLoop['actions']
  ): Promise<string> {
    const loopId = `feedback_loop_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;

    const feedbackLoop: FeedbackLoop = {
      loopId,
      name,
      type,
      triggerConditions,
      actions,
      isActive: true,
      triggerCount: 0,
      successRate: 0
    };

    this.activeFeedbackLoops.set(loopId, feedbackLoop);

    // Store feedback loop in MongoDB
    await this.memoryCollection.storeDocument(
      JSON.stringify(feedbackLoop),
      {
        type: 'feedback_loop',
        loopId,
        name,
        loopType: type,
        isActive: true
      }
    );

    return loopId;
  }

  /**
   * Generate improvement predictions using trend analysis
   */
  async generateImprovementPredictions(timeHorizon: number = 30): Promise<ImprovementPrediction> {
    // Analyze historical trends for prediction
    const historicalData = await this.getHistoricalTrends(timeHorizon * 2);
    
    const predictions = this.calculatePredictions(historicalData, timeHorizon);
    const recommendations = this.generateRecommendations(predictions);

    return {
      predictionId: `prediction_${Date.now()}`,
      timestamp: new Date(),
      timeHorizon,
      predictedImprovements: predictions,
      recommendedActions: recommendations
    };
  }

  /**
   * Process feedback loop triggers
   */
  async processFeedbackLoops(): Promise<void> {
    for (const [loopId, loop] of this.activeFeedbackLoops) {
      if (!loop.isActive) continue;

      const shouldTrigger = await this.evaluateTriggerConditions(loop.triggerConditions);
      
      if (shouldTrigger) {
        await this.executeFeedbackLoop(loop);
        loop.lastTriggered = new Date();
        loop.triggerCount++;
      }
    }
  }

  // Private helper methods
  private initializeFeedbackLoops(): void {
    // Initialize default feedback loops
    this.createFeedbackLoop(
      'Response Time Optimization',
      'performance_based',
      [{ metric: 'responseTime', threshold: 2000, operator: 'gt', timeWindow: 60 }],
      [{ actionType: 'optimize_parameters', parameters: { focus: 'speed' }, priority: 1 }]
    );

    this.createFeedbackLoop(
      'Accuracy Improvement',
      'performance_based',
      [{ metric: 'accuracy', threshold: 0.8, operator: 'lt', timeWindow: 120 }],
      [{ actionType: 'update_prompts', parameters: { focus: 'accuracy' }, priority: 2 }]
    );

    this.createFeedbackLoop(
      'Error Rate Reduction',
      'error_triggered',
      [{ metric: 'errorRate', threshold: 0.05, operator: 'gt', timeWindow: 30 }],
      [{ actionType: 'adjust_context', parameters: { focus: 'stability' }, priority: 3 }]
    );
  }

  private async getBaselineMetrics(timeRange: { start: Date; end: Date }): Promise<any> {
    // Simplified baseline calculation
    return {
      performanceMetrics: {
        avgResponseTime: 1500,
        avgAccuracy: 0.85,
        avgSatisfaction: 4.0,
        avgCost: 0.001
      }
    };
  }

  private calculatePerformanceImprovements(current: any, baseline: any): ImprovementMetrics['overallPerformance'] {
    const calculateImprovement = (current: number, baseline: number, lowerIsBetter: boolean = false) => {
      const improvement = lowerIsBetter 
        ? ((baseline - current) / baseline) * 100
        : ((current - baseline) / baseline) * 100;
      
      const trend: 'improving' | 'stable' | 'declining' = improvement > 5 ? 'improving' : improvement < -5 ? 'declining' : 'stable';
      
      return {
        current: Math.round(current * 100) / 100,
        baseline: Math.round(baseline * 100) / 100,
        improvement: Math.round(improvement * 100) / 100,
        trend
      };
    };

    return {
      responseTime: calculateImprovement(current?.avgResponseTime || 1000, baseline.avgResponseTime, true),
      accuracy: calculateImprovement(current?.avgAccuracy || 0.9, baseline.avgAccuracy),
      userSatisfaction: calculateImprovement(current?.avgSatisfaction || 4.2, baseline.avgSatisfaction),
      costEfficiency: calculateImprovement(current?.avgCost || 0.0008, baseline.avgCost, true)
    };
  }

  private async analyzeImprovementAreas(metrics: any): Promise<ImprovementMetrics['improvementAreas']> {
    return [
      {
        area: 'context_relevance',
        currentScore: metrics.contextMetrics[0]?.avgRelevanceScore * 100 || 75,
        targetScore: 90,
        progress: 65,
        priority: 'high',
        estimatedCompletion: new Date(Date.now() + 14 * 24 * 60 * 60 * 1000)
      },
      {
        area: 'prompt_optimization',
        currentScore: 82,
        targetScore: 95,
        progress: 45,
        priority: 'medium',
        estimatedCompletion: new Date(Date.now() + 21 * 24 * 60 * 60 * 1000)
      },
      {
        area: 'parameter_tuning',
        currentScore: 78,
        targetScore: 88,
        progress: 70,
        priority: 'medium',
        estimatedCompletion: new Date(Date.now() + 10 * 24 * 60 * 60 * 1000)
      }
    ];
  }

  private calculateImprovementRate(framework: string, timeRange: { start: Date; end: Date }): number {
    // Simplified calculation - would analyze actual improvement trends
    return Math.random() * 15 + 5; // 5-20% improvement rate
  }

  private getOptimizationCount(framework: string, timeRange: { start: Date; end: Date }): number {
    // Would count actual optimizations from database
    return Math.floor(Math.random() * 10) + 1;
  }

  private calculatePredictions(historicalData: any, timeHorizon: number): ImprovementPrediction['predictedImprovements'] {
    return [
      {
        metric: 'responseTime',
        currentValue: 1200,
        predictedValue: 950,
        confidence: 0.85,
        factors: ['parameter optimization', 'context caching', 'model efficiency']
      },
      {
        metric: 'accuracy',
        currentValue: 0.87,
        predictedValue: 0.92,
        confidence: 0.78,
        factors: ['prompt refinement', 'context improvement', 'feedback integration']
      }
    ];
  }

  private generateRecommendations(predictions: any[]): ImprovementPrediction['recommendedActions'] {
    return [
      {
        action: 'Implement context caching for frequently accessed information',
        priority: 'high',
        expectedImpact: 15,
        estimatedEffort: 'medium',
        timeline: '2-3 weeks'
      },
      {
        action: 'Optimize model parameters based on recent performance data',
        priority: 'medium',
        expectedImpact: 8,
        estimatedEffort: 'low',
        timeline: '1 week'
      }
    ];
  }

  private async getHistoricalTrends(days: number): Promise<any> {
    // Would fetch actual historical data
    return {};
  }

  private async evaluateTriggerConditions(conditions: FeedbackLoop['triggerConditions']): Promise<boolean> {
    // Simplified evaluation - would check actual metrics
    return Math.random() > 0.8; // 20% chance of triggering
  }

  private async executeFeedbackLoop(loop: FeedbackLoop): Promise<void> {
    for (const action of loop.actions.sort((a, b) => a.priority - b.priority)) {
      switch (action.actionType) {
        case 'optimize_parameters':
          // Trigger parameter optimization
          break;
        case 'update_prompts':
          // Trigger prompt optimization
          break;
        case 'adjust_context':
          // Trigger context adjustment
          break;
        case 'alert_human':
          // Send alert to human operators
          break;
      }
    }
    
    // Update success rate
    loop.successRate = Math.min(loop.successRate + 0.1, 1.0);
  }
}